OpenRaiser/Intern-Atlas huggingface dataset:
Progressive Cleaning and Mining of Uncertain Smart Water Meter Data
Venue: arXiv cs.DB 2020
Year: 2020

Several municipalities have recently installed wireless 'smart' water meters that allow functionalities such as demand response, leak alerts, identification of characteristic demand patterns, and detailed consumption analysis. To achieve these benefits, the meter data needs to be error-free, which is not necessarily available in practice, due to 'dirtiness' or 'uncertainty' of data, which is mostly unavoidable. The focus of this paper is to investigate practical solutions to mine uncertain data for reliable results and to evaluate the impact of dirty data on filters. This evaluation would eventually lead to valuable information, which can be used for educated decision making on water planning strategies. We perform a systematic study of the errors existing in a large-scale smart water meter deployments, which is helpful to better understand the nature of errors. Identifying customers contributing to a load peak is used as the main filter. The filter outputs are then combined with the domain expert knowledge to evaluate their accuracy and validity and also to look for potential errors. After discovering each error, we analyze its trails in the data and track back its source, which would eventually lead to the removal of the error or dealing with it accordingly. This procedure is applied progressively to ensure that all detectable errors are discovered and characterized in the data model. We evaluate the performance of the proposed approach using the smart water meter consumption data obtained from the City of Abbotsford, British Columbia, Canada. We present the results of both unprocessed and cleaned data and analyze, in detail, the sensitivity of the selected filter to the errors.
Breaking Band: A Breakdown of High-performance Communication
Venue: arXiv cs.DC 2020
Year: 2020

The critical path of internode communication on large-scale systems is composed of multiple components. When a supercomputing application initiates the transfer of a message using a high-level communication routine such as an MPI_Send, the payload of the message traverses multiple software stacks, the I/O subsystem on both the host and target nodes, and network components such as the switch. In this paper, we analyze where, why, and how much time is spent on the critical path of communication by modeling the overall injection overhead and end-to-end latency of a system. We focus our analysis on the performance of small messages since fine-grained communication is becoming increasingly important with the growing trend of an increasing number of cores per node. The analytical models present an accurate and detailed breakdown of time spent in internode communication. We validate the models on Arm ThunderX2-based servers connected with Mellanox InfiniBand. This is the first work of this kind on Arm. Alongside our breakdown, we describe the methodology to measure the time spent in each component so that readers with access to precise CPU timers and a PCIe analyzer can measure breakdowns on systems of their interest. Such a breakdown is crucial for software developers, system architects, and researchers to guide their optimization efforts. As researchers ourselves, we use the breakdown to simulate the impacts and discuss the likelihoods of a set of optimizations that target the bottlenecks in today's high-performance communication.
Refining Constructive Hybrid Games
Venue: arXiv cs.LO 2020
Year: 2020

We extend the constructive differential game logic (CdGL) of hybrid games with a refinement connective that relates two hybrid games. We use this connective to prove a folk theorem relating hybrid games to hybrid systems.
Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids
Venue: arXiv cs.LG 2020
Year: 2020

Microgrids (MGs) are small, local power grids that can operate independently from the larger utility grid. Combined with the Internet of Things (IoT), a smart MG can leverage the sensory data and machine learning techniques for intelligent energy management. This paper focuses on deep reinforcement learning (DRL)-based energy dispatch for IoT-driven smart isolated MGs with diesel generators (DGs), photovoltaic (PV) panels, and a battery. A finite-horizon Partial Observable Markov Decision Process (POMDP) model is formulated and solved by learning from historical data to capture the uncertainty in future electricity consumption and renewable power generation. In order to deal with the instability problem of DRL algorithms and unique characteristics of finite-horizon models, two novel DRL algorithms, namely, finite-horizon deep deterministic policy gradient (FH-DDPG) and finite-horizon recurrent deterministic policy gradient (FH-RDPG), are proposed to derive energy dispatch policies with and without fully observable state information. A case study using real isolated MG data is performed, where the performance of the proposed algorithms are compared with the other baseline DRL and non-DRL algorithms. Moreover, the impact of uncertainties on MG performance is decoupled into two levels and evaluated respectively.
Translating Web Search Queries into Natural Language Questions
Venue: arXiv cs.CL 2020
Year: 2020

Users often query a search engine with a specific question in mind and often these queries are keywords or sub-sentential fragments. For example, if the users want to know the answer for "What's the capital of USA", they will most probably query "capital of USA" or "USA capital" or some keyword-based variation of this. For example, for the user entered query "capital of USA", the most probable question intent is "What's the capital of USA?". In this paper, we are proposing a method to generate well-formed natural language question from a given keyword-based query, which has the same question intent as the query. Conversion of keyword-based web query into a well-formed question has lots of applications, with some of them being in search engines, Community Question Answering (CQA) website and bots communication. We found a synergy between query-to-question problem with standard machine translation(MT) task. We have used both Statistical MT (SMT) and Neural MT (NMT) models to generate the questions from the query. We have observed that MT models perform well in terms of both automatic and human evaluation.
Temporal Probability Calibration
Venue: arXiv cs.LG 2020
Year: 2020

In many applications, accurate class probability estimates are required, but many types of models produce poor quality probability estimates despite achieving acceptable classification accuracy. Even though probability calibration has been a hot topic of research in recent times, the majority of this has investigated non-sequential data. In this paper, we consider calibrating models that produce class probability estimates from sequences of data, focusing on the case where predictions are obtained from incomplete sequences. We show that traditional calibration techniques are not sufficiently expressive for this task, and propose methods that adapt calibration schemes depending on the length of an input sequence. Experimental evaluation shows that the proposed methods are often substantially more effective at calibrating probability estimates from modern sequential architectures for incomplete sequences across a range of application domains.
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

Lane-change maneuvers are commonly executed by drivers to follow a certain routing plan, overtake a slower vehicle, adapt to a merging lane ahead, etc. However, improper lane change behaviors can be a major cause of traffic flow disruptions and even crashes. While many rule-based methods have been proposed to solve lane change problems for autonomous driving, they tend to exhibit limited performance due to the uncertainty and complexity of the driving environment. Machine learning-based methods offer an alternative approach, as Deep reinforcement learning (DRL) has shown promising success in many application domains including robotic manipulation, navigation, and playing video games. However, applying DRL to autonomous driving still faces many practical challenges in terms of slow learning rates, sample inefficiency, and safety concerns. In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning, which shows great advantages in learning efficiency while still maintaining stable performance. The trained agent is able to learn a smooth, safe, and efficient driving policy to make lane-change decisions (i.e. when and how) in a challenging situation such as dense traffic scenarios. The effectiveness of the proposed policy is validated by using metrics of task success rate and collision rate. The simulation results demonstrate the lane change maneuvers can be efficiently learned and executed in a safe, smooth, and efficient manner.
Formalising and verifying smart contracts with Solidifier: a bounded model checker for Solidity
Venue: arXiv cs.PL 2020
Year: 2020

The exploitation of smart-contract vulnerabilities can have catastrophic consequences such as the loss of millions of pounds worth of crypto assets. Formal verification can be a useful tool in identifying vulnerabilities and proving that they have been fixed. In this paper, we present a formalisation of Solidity and the Ethereum blockchain using the Solid language and its blockchain; a Solid program is obtained by explicating/desugaring a Solidity program. We make some abstractions that over-approximate the way in which Solidity/Ethereum behave. Based on this formalisation, we create Solidifier: a bounded model checker for Solidity. It translates Solid into Boogie, an intermediate verification language, that is later verified using Corral, a bounded model checker for Boogie. Unlike much of the work in this area, we do not try to find specific behavioural/code patterns that might lead to vulnerabilities. Instead, we provide a tool to find errors/bad states, i.e. program states that do not conform with the intent of the developer. Such a bad state, be it a vulnerability or not, might be reached through the execution of specific known code patterns or through behaviours that have not been anticipated.
Impact of the Interaction Network on the Dynamics of Word-of-Mouth with Information Seeking
Venue: arXiv cs.SI 2020
Year: 2020

Word-of-Mouth refers to the dynamics of interpersonal communication occurring during the diffusion of innovations (novel practices, ideas or products). According to field studies, word-of-mouth is made of both information seeking and proactive communication: individuals first become aware of the existence of an innovation, then start actively seeking out for the expert knowledge required to evaluate the innovation; when they hold the expert knowledge, they might start promoting it pro-actively. Successful diffusion of innovation requires the individuals to hold both awareness and expert knowledge, so they can evaluate the innovation and use it properly. A computational model "USA/IPK" was recently proposed to study the role and impact of information seeking on the dynamics of word-of-mouth. We propose here an analysis of the impact of the network of interaction on the dynamics of this model. We compare the dynamics of the model over networks generated with different algorithms with the original dynamics. The results demonstrate the dynamics of the model are similar across tested networks, with the noticeable exception of the efficiency of the diffusion which varies between networks having similar densities and sizes.
On List k-Coloring Convex Bipartite Graphs
Venue: arXiv cs.CC 2020
Year: 2020

List k-Coloring (Li k-Col) is the decision problem asking if a given graph admits a proper coloring compatible with a given list assignment to its vertices with colors in {1,2,..,k}. The problem is known to be NP-hard even for k=3 within the class of 3-regular planar bipartite graphs and for k=4 within the class of chordal bipartite graphs. In 2015, Huang, Johnson and Paulusma asked for the complexity of Li 3-Col in the class of chordal bipartite graphs. In this paper we give a partial answer to this question by showing that Li k-Col is polynomial in the class of convex bipartite graphs. We show first that biconvex bipartite graphs admit a multichain ordering, extending the classes of graphs where a polynomial algorithm of Enright, Stewart and Tardos (2014) can be applied to the problem. We provide a dynamic programming algorithm to solve the Li k-Col in the calss of convex bipartite graphs. Finally we show how our algorithm can be modified to solve the more general Li H-Col problem on convex bipartite graphs.
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks
Venue: arXiv cs.LG 2020
Year: 2020

In recent years, a variety of effective neural network-based methods for anomaly and cyber attack detection in industrial control systems (ICSs) have been demonstrated in the literature. Given their successful implementation and widespread use, there is a need to study adversarial attacks on such detection methods to better protect the systems that depend upon them. The extensive research performed on adversarial attacks on image and malware classification has little relevance to the physical system state prediction domain, which most of the ICS attack detection systems belong to. Moreover, such detection systems are typically retrained using new data collected from the monitored system, thus the threat of adversarial data poisoning is significant, however this threat has not yet been addressed by the research community. In this paper, we present the first study focused on poisoning attacks on online-trained autoencoder-based attack detectors. We propose two algorithms for generating poison samples, an interpolation-based algorithm and a back-gradient optimization-based algorithm, which we evaluate on both synthetic and real-world ICS data. We demonstrate that the proposed algorithms can generate poison samples that cause the target attack to go undetected by the autoencoder detector, however the ability to poison the detector is limited to a small set of attack types and magnitudes. When the poison-generating algorithms are applied to the popular SWaT dataset, we show that the autoencoder detector trained on the physical system state data is resilient to poisoning in the face of all ten of the relevant attacks in the dataset. This finding suggests that neural network-based attack detectors used in the cyber-physical domain are more robust to poisoning than in other problem domains, such as malware detection and image processing.
Nonparametric Regression Quantum Neural Networks
Venue: arXiv cs.ET 2020
Year: 2020

In two pervious papers \cite{dndiep3}, \cite{dndiep4}, the first author constructed the least square quantum neural networks (LS-QNN), and ploynomial interpolation quantum neural networks ( PI-QNN), parametrico-stattistical QNN like: leanr regrassion quantum neural networks (LR-QNN), polynomial regression quantum neural networks (PR-QNN), chi-squared quantum neural netowrks ($χ^2$-QNN). We observed that the method works also in the cases by using nonparametric statistics. In this paper we analyze and implement the nonparametric tests on QNN such as: linear nonparametric regression quantum neural networks (LNR-QNN), polynomial nonparametric regression quantum neural networks (PNR-QNN). The implementation is constructed through the Gauss-Jordan Elimination quantum neural networks (GJE-QNN).The training rule is to use the high probability confidence regions or intervals.
Learning Implicit Generative Models with Theoretical Guarantees
Venue: arXiv cs.LG 2020
Year: 2020

We propose a \textbf{uni}fied \textbf{f}ramework for \textbf{i}mplicit \textbf{ge}nerative \textbf{m}odeling (UnifiGem) with theoretical guarantees by integrating approaches from optimal transport, numerical ODE, density-ratio (density-difference) estimation and deep neural networks. First, the problem of implicit generative learning is formulated as that of finding the optimal transport map between the reference distribution and the target distribution, which is characterized by a totally nonlinear Monge-Ampère equation. Interpreting the infinitesimal linearization of the Monge-Ampère equation from the perspective of gradient flows in measure spaces leads to the continuity equation or the McKean-Vlasov equation. We then solve the McKean-Vlasov equation numerically using the forward Euler iteration, where the forward Euler map depends on the density ratio (density difference) between the distribution at current iteration and the underlying target distribution. We further estimate the density ratio (density difference) via deep density-ratio (density-difference) fitting and derive explicit upper bounds on the estimation error. Experimental results on both synthetic datasets and real benchmark datasets support our theoretical findings and demonstrate the effectiveness of UnifiGem.
Understanding the effects of artifacts on automated polyp detection and incorporating that knowledge via learning without forgetting
Venue: arXiv cs.LG 2020
Year: 2020

Survival rates for colorectal cancer are higher when polyps are detected at an early stage and can be removed before they develop into malignant tumors. Automated polyp detection, which is dominated by deep learning based methods, seeks to improve early detection of polyps. However, current efforts rely heavily on the size and quality of the training datasets. The quality of these datasets often suffers from various image artifacts that affect the visibility and hence, the detection rate. In this work, we conducted a systematic analysis to gain a better understanding of how artifacts affect automated polyp detection. We look at how six different artifact classes, and their location in an image, affect the performance of a RetinaNet based polyp detection model. We found that, depending on the artifact class, they can either benefit or harm the polyp detector. For instance, bubbles are often misclassified as polyps, while specular reflections inside of a polyp region can improve detection capabilities. We then investigated different strategies, such as a learning without forgetting framework, to leverage artifact knowledge to improve automated polyp detection. Our results show that such models can mitigate some of the harmful effects of artifacts, but require more work to significantly improve polyp detection capabilities.
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
Venue: arXiv cs.CV 2020
Year: 2020

Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women. It is often speculated that lower accuracy for women is caused by under-representation in the training data. This work investigates female under-representation in the training data is truly the cause of lower accuracy for females on test data. Using a state-of-the-art deep CNN, three different loss functions, and two training datasets, we train each on seven subsets with different male/female ratios, totaling forty two trainings, that are tested on three different datasets. Results show that (1) gender balance in the training data does not translate into gender balance in the test accuracy, (2) the "gender gap" in test accuracy is not minimized by a gender-balanced training set, but by a training set with more male images than female images, and (3) training to minimize the accuracy gap does not result in highest female, male or average accuracy
A polynomial algorithm for maxmin and minmax envy-free rent division on a soft budget
Venue: arXiv cs.GT 2020
Year: 2020

The current practice of envy-free rent division, lead by the fair allocation website Spliddit, is based on quasi-linear preferences. These preferences rule out agents' well documented financial constraints. To resolve this issue we consider piece-wise linear budget constrained preferences. These preferences admit differences in agents' marginal disutility of paying rent below and above a given reference, i.e., a soft budget. We construct a polynomial algorithm to calculate a maxmin utility envy-free allocation, and other related solutions, in this domain.
Above Surface Interaction for Multiscale Navigation in Mobile Virtual Reality
Venue: arXiv cs.HC 2020
Year: 2020

Virtual Reality enables the exploration of large information spaces. In physically constrained spaces such as airplanes or buses, controller-based or mid-air interaction in mobile Virtual Reality can be challenging. Instead, the input space on and above touch-screen enabled devices such as smartphones or tablets could be employed for Virtual Reality interaction in those spaces. In this context, we compared an above surface interaction technique with traditional 2D on-surface input for navigating large planar information spaces such as maps in a controlled user study (n = 20). We find that our proposed above surface interaction technique results in significantly better performance and user preference compared to pinch-to-zoom and drag-to-pan when navigating planar information spaces.
Achievable Rate of Multi-Antenna WSRNs with EH Constraint in the presence of a Jammer
Venue: arXiv cs.IT 2020
Year: 2020

In this paper, the rate-energy region is studied for the wireless sensor relay network (WSRN) with energy harvesting in the presence of a jammer. In the model, a source communicates to a destination equipped with a single antenna with energy harvesting constraint through a multi-antenna cooperative relay under beamforming. Meanwhile, there is a jammer intended to disturb the communication. The relay works in half-duplex mode and knows all the channel state information (CSI). When beamforming is employed at the relay, the network can be modeled as an equivalent Gaussian arbitrarily varying channel (GAVC). We characterize the achievable rate-energy region. Since the problem is non-convex, we present three methods to transform it into a semi-definite programming problem (SDP), and the closed-form expression for two special boundary points of the rate-energy region is obtained. Finally, the simulations show the rate-energy region and the anti-jamming performance of the proposed scheme.
Means-fit effectivity
Venue: arXiv cs.LO 2020
Year: 2020

Historically, the notion of effective algorithm is closely related to the Church-Turing thesis. But effectivity imposes no restriction on computation time or any other resource; in that sense, it is incompatible with engineering or physics. We propose a natural generalization of it, means-fitting effectivity, which is effectivity relative to the (physical or abstract) underlying machinery of the algorithm. This machinery varies from one class of algorithms to another. Think for example of ruler-and-compass algorithms, arithmetical algorithms, and Blum-Shub-Smale algorithms. We believe that means-fitting effectivity is meaningful and useful independently of the Church-Turing thesis. Means-fitting effectivity is definable, at least in the theory of abstract state machines (ASMs). The definition elucidates original effectivity as well. Familiarity with the ASM theory is not assumed. We tried to make the paper self-contained.